Klasifikasi Breast Cancer dengan Naive Bayes
yohanesegipratama
2022-11-16
Objectives
Analisis ini bertujuan untuk mengamati fitur mana yang paling membantu dalam memprediksi kanker ganas atau jinak dan untuk melihat tren umum yang dapat membantu kita dalam pemilihan model dan pemilihan parameter hiper. Tujuannya adalah untuk mengklasifikasikan apakah kanker payudara tersebut jinak atau ganas. Untuk mencapai ini saya menggunakan metode naive bayes
library
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2## Warning: package 'ggplot2' was built under R version 4.2.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(caret)## Warning: package 'caret' was built under R version 4.2.2## Loading required package: lattice
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:purrr':
##
## liftlibrary(e1071)## Warning: package 'e1071' was built under R version 4.2.2library(visdat)## Warning: package 'visdat' was built under R version 4.2.2library(cowplot)## Warning: package 'cowplot' was built under R version 4.2.2library(corrplot)## corrplot 0.92 loadedload data
df = read.csv('data_input/data.csv')
head(df)## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## 1 842302 M 17.99 10.38 122.80 1001.0
## 2 842517 M 20.57 17.77 132.90 1326.0
## 3 84300903 M 19.69 21.25 130.00 1203.0
## 4 84348301 M 11.42 20.38 77.58 386.1
## 5 84358402 M 20.29 14.34 135.10 1297.0
## 6 843786 M 12.45 15.70 82.57 477.1
## smoothness_mean compactness_mean concavity_mean concave.points_mean
## 1 0.11840 0.27760 0.3001 0.14710
## 2 0.08474 0.07864 0.0869 0.07017
## 3 0.10960 0.15990 0.1974 0.12790
## 4 0.14250 0.28390 0.2414 0.10520
## 5 0.10030 0.13280 0.1980 0.10430
## 6 0.12780 0.17000 0.1578 0.08089
## symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se
## 1 0.2419 0.07871 1.0950 0.9053 8.589
## 2 0.1812 0.05667 0.5435 0.7339 3.398
## 3 0.2069 0.05999 0.7456 0.7869 4.585
## 4 0.2597 0.09744 0.4956 1.1560 3.445
## 5 0.1809 0.05883 0.7572 0.7813 5.438
## 6 0.2087 0.07613 0.3345 0.8902 2.217
## area_se smoothness_se compactness_se concavity_se concave.points_se
## 1 153.40 0.006399 0.04904 0.05373 0.01587
## 2 74.08 0.005225 0.01308 0.01860 0.01340
## 3 94.03 0.006150 0.04006 0.03832 0.02058
## 4 27.23 0.009110 0.07458 0.05661 0.01867
## 5 94.44 0.011490 0.02461 0.05688 0.01885
## 6 27.19 0.007510 0.03345 0.03672 0.01137
## symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst
## 1 0.03003 0.006193 25.38 17.33 184.60
## 2 0.01389 0.003532 24.99 23.41 158.80
## 3 0.02250 0.004571 23.57 25.53 152.50
## 4 0.05963 0.009208 14.91 26.50 98.87
## 5 0.01756 0.005115 22.54 16.67 152.20
## 6 0.02165 0.005082 15.47 23.75 103.40
## area_worst smoothness_worst compactness_worst concavity_worst
## 1 2019.0 0.1622 0.6656 0.7119
## 2 1956.0 0.1238 0.1866 0.2416
## 3 1709.0 0.1444 0.4245 0.4504
## 4 567.7 0.2098 0.8663 0.6869
## 5 1575.0 0.1374 0.2050 0.4000
## 6 741.6 0.1791 0.5249 0.5355
## concave.points_worst symmetry_worst fractal_dimension_worst X
## 1 0.2654 0.4601 0.11890 NA
## 2 0.1860 0.2750 0.08902 NA
## 3 0.2430 0.3613 0.08758 NA
## 4 0.2575 0.6638 0.17300 NA
## 5 0.1625 0.2364 0.07678 NA
## 6 0.1741 0.3985 0.12440 NAData Preparation
Check Missing Value
colSums(is.na(df))## id diagnosis radius_mean
## 0 0 0
## texture_mean perimeter_mean area_mean
## 0 0 0
## smoothness_mean compactness_mean concavity_mean
## 0 0 0
## concave.points_mean symmetry_mean fractal_dimension_mean
## 0 0 0
## radius_se texture_se perimeter_se
## 0 0 0
## area_se smoothness_se compactness_se
## 0 0 0
## concavity_se concave.points_se symmetry_se
## 0 0 0
## fractal_dimension_se radius_worst texture_worst
## 0 0 0
## perimeter_worst area_worst smoothness_worst
## 0 0 0
## compactness_worst concavity_worst concave.points_worst
## 0 0 0
## symmetry_worst fractal_dimension_worst X
## 0 0 569glimpse(df)## Rows: 569
## Columns: 33
## $ id <int> 842302, 842517, 84300903, 84348301, 84358402, …
## $ diagnosis <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "…
## $ radius_mean <dbl> 17.990, 20.570, 19.690, 11.420, 20.290, 12.450…
## $ texture_mean <dbl> 10.38, 17.77, 21.25, 20.38, 14.34, 15.70, 19.9…
## $ perimeter_mean <dbl> 122.80, 132.90, 130.00, 77.58, 135.10, 82.57, …
## $ area_mean <dbl> 1001.0, 1326.0, 1203.0, 386.1, 1297.0, 477.1, …
## $ smoothness_mean <dbl> 0.11840, 0.08474, 0.10960, 0.14250, 0.10030, 0…
## $ compactness_mean <dbl> 0.27760, 0.07864, 0.15990, 0.28390, 0.13280, 0…
## $ concavity_mean <dbl> 0.30010, 0.08690, 0.19740, 0.24140, 0.19800, 0…
## $ concave.points_mean <dbl> 0.14710, 0.07017, 0.12790, 0.10520, 0.10430, 0…
## $ symmetry_mean <dbl> 0.2419, 0.1812, 0.2069, 0.2597, 0.1809, 0.2087…
## $ fractal_dimension_mean <dbl> 0.07871, 0.05667, 0.05999, 0.09744, 0.05883, 0…
## $ radius_se <dbl> 1.0950, 0.5435, 0.7456, 0.4956, 0.7572, 0.3345…
## $ texture_se <dbl> 0.9053, 0.7339, 0.7869, 1.1560, 0.7813, 0.8902…
## $ perimeter_se <dbl> 8.589, 3.398, 4.585, 3.445, 5.438, 2.217, 3.18…
## $ area_se <dbl> 153.40, 74.08, 94.03, 27.23, 94.44, 27.19, 53.…
## $ smoothness_se <dbl> 0.006399, 0.005225, 0.006150, 0.009110, 0.0114…
## $ compactness_se <dbl> 0.049040, 0.013080, 0.040060, 0.074580, 0.0246…
## $ concavity_se <dbl> 0.05373, 0.01860, 0.03832, 0.05661, 0.05688, 0…
## $ concave.points_se <dbl> 0.015870, 0.013400, 0.020580, 0.018670, 0.0188…
## $ symmetry_se <dbl> 0.03003, 0.01389, 0.02250, 0.05963, 0.01756, 0…
## $ fractal_dimension_se <dbl> 0.006193, 0.003532, 0.004571, 0.009208, 0.0051…
## $ radius_worst <dbl> 25.38, 24.99, 23.57, 14.91, 22.54, 15.47, 22.8…
## $ texture_worst <dbl> 17.33, 23.41, 25.53, 26.50, 16.67, 23.75, 27.6…
## $ perimeter_worst <dbl> 184.60, 158.80, 152.50, 98.87, 152.20, 103.40,…
## $ area_worst <dbl> 2019.0, 1956.0, 1709.0, 567.7, 1575.0, 741.6, …
## $ smoothness_worst <dbl> 0.1622, 0.1238, 0.1444, 0.2098, 0.1374, 0.1791…
## $ compactness_worst <dbl> 0.6656, 0.1866, 0.4245, 0.8663, 0.2050, 0.5249…
## $ concavity_worst <dbl> 0.71190, 0.24160, 0.45040, 0.68690, 0.40000, 0…
## $ concave.points_worst <dbl> 0.26540, 0.18600, 0.24300, 0.25750, 0.16250, 0…
## $ symmetry_worst <dbl> 0.4601, 0.2750, 0.3613, 0.6638, 0.2364, 0.3985…
## $ fractal_dimension_worst <dbl> 0.11890, 0.08902, 0.08758, 0.17300, 0.07678, 0…
## $ X <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…- insight Kumpulan data kanker payudara Wisconsin berisi 569 entri yang berasal dari analisis gambar aspirasi jarum halus dari massa payudara. Kolom pertama berisi id unik dan kolom kedua berisi diagnosis.
Tidak ada nilai yang hilang kecuali kolom 33 yang terlihat kosong.
# select features
df_selected <- df[1:32]
# drop id variable
Matrix <- df_selected%>%
select(-c("id"))%>%
# convert diagnosis to numerical
mutate(diagnosis = case_when(
diagnosis == "M"~1,
diagnosis == "B"~0,
))%>%
# convert to matrix
as.matrix()
# add id column as names
row.names(Matrix) <- df_selected$id
head(Matrix)## diagnosis radius_mean texture_mean perimeter_mean area_mean
## 842302 1 17.99 10.38 122.80 1001.0
## 842517 1 20.57 17.77 132.90 1326.0
## 84300903 1 19.69 21.25 130.00 1203.0
## 84348301 1 11.42 20.38 77.58 386.1
## 84358402 1 20.29 14.34 135.10 1297.0
## 843786 1 12.45 15.70 82.57 477.1
## smoothness_mean compactness_mean concavity_mean concave.points_mean
## 842302 0.11840 0.27760 0.3001 0.14710
## 842517 0.08474 0.07864 0.0869 0.07017
## 84300903 0.10960 0.15990 0.1974 0.12790
## 84348301 0.14250 0.28390 0.2414 0.10520
## 84358402 0.10030 0.13280 0.1980 0.10430
## 843786 0.12780 0.17000 0.1578 0.08089
## symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se
## 842302 0.2419 0.07871 1.0950 0.9053 8.589
## 842517 0.1812 0.05667 0.5435 0.7339 3.398
## 84300903 0.2069 0.05999 0.7456 0.7869 4.585
## 84348301 0.2597 0.09744 0.4956 1.1560 3.445
## 84358402 0.1809 0.05883 0.7572 0.7813 5.438
## 843786 0.2087 0.07613 0.3345 0.8902 2.217
## area_se smoothness_se compactness_se concavity_se concave.points_se
## 842302 153.40 0.006399 0.04904 0.05373 0.01587
## 842517 74.08 0.005225 0.01308 0.01860 0.01340
## 84300903 94.03 0.006150 0.04006 0.03832 0.02058
## 84348301 27.23 0.009110 0.07458 0.05661 0.01867
## 84358402 94.44 0.011490 0.02461 0.05688 0.01885
## 843786 27.19 0.007510 0.03345 0.03672 0.01137
## symmetry_se fractal_dimension_se radius_worst texture_worst
## 842302 0.03003 0.006193 25.38 17.33
## 842517 0.01389 0.003532 24.99 23.41
## 84300903 0.02250 0.004571 23.57 25.53
## 84348301 0.05963 0.009208 14.91 26.50
## 84358402 0.01756 0.005115 22.54 16.67
## 843786 0.02165 0.005082 15.47 23.75
## perimeter_worst area_worst smoothness_worst compactness_worst
## 842302 184.60 2019.0 0.1622 0.6656
## 842517 158.80 1956.0 0.1238 0.1866
## 84300903 152.50 1709.0 0.1444 0.4245
## 84348301 98.87 567.7 0.2098 0.8663
## 84358402 152.20 1575.0 0.1374 0.2050
## 843786 103.40 741.6 0.1791 0.5249
## concavity_worst concave.points_worst symmetry_worst
## 842302 0.7119 0.2654 0.4601
## 842517 0.2416 0.1860 0.2750
## 84300903 0.4504 0.2430 0.3613
## 84348301 0.6869 0.2575 0.6638
## 84358402 0.4000 0.1625 0.2364
## 843786 0.5355 0.1741 0.3985
## fractal_dimension_worst
## 842302 0.11890
## 842517 0.08902
## 84300903 0.08758
## 84348301 0.17300
## 84358402 0.07678
## 843786 0.12440is.numeric(Matrix)## [1] TRUEScalling
mean_sd <- cbind(as.data.frame(colMeans(Matrix)), as.data.frame(apply(Matrix, 2, sd)))
colnames(mean_sd) <- c("Mean","SD")
mean_sd## Mean SD
## diagnosis 3.725835e-01 4.839180e-01
## radius_mean 1.412729e+01 3.524049e+00
## texture_mean 1.928965e+01 4.301036e+00
## perimeter_mean 9.196903e+01 2.429898e+01
## area_mean 6.548891e+02 3.519141e+02
## smoothness_mean 9.636028e-02 1.406413e-02
## compactness_mean 1.043410e-01 5.281276e-02
## concavity_mean 8.879932e-02 7.971981e-02
## concave.points_mean 4.891915e-02 3.880284e-02
## symmetry_mean 1.811619e-01 2.741428e-02
## fractal_dimension_mean 6.279761e-02 7.060363e-03
## radius_se 4.051721e-01 2.773127e-01
## texture_se 1.216853e+00 5.516484e-01
## perimeter_se 2.866059e+00 2.021855e+00
## area_se 4.033708e+01 4.549101e+01
## smoothness_se 7.040979e-03 3.002518e-03
## compactness_se 2.547814e-02 1.790818e-02
## concavity_se 3.189372e-02 3.018606e-02
## concave.points_se 1.179614e-02 6.170285e-03
## symmetry_se 2.054230e-02 8.266372e-03
## fractal_dimension_se 3.794904e-03 2.646071e-03
## radius_worst 1.626919e+01 4.833242e+00
## texture_worst 2.567722e+01 6.146258e+00
## perimeter_worst 1.072612e+02 3.360254e+01
## area_worst 8.805831e+02 5.693570e+02
## smoothness_worst 1.323686e-01 2.283243e-02
## compactness_worst 2.542650e-01 1.573365e-01
## concavity_worst 2.721885e-01 2.086243e-01
## concave.points_worst 1.146062e-01 6.573234e-02
## symmetry_worst 2.900756e-01 6.186747e-02
## fractal_dimension_worst 8.394582e-02 1.806127e-02ScaledMatrix <- scale(Matrix)
head(ScaledMatrix)## diagnosis radius_mean texture_mean perimeter_mean area_mean
## 842302 1.296535 1.0960995 -2.0715123 1.2688173 0.9835095
## 842517 1.296535 1.8282120 -0.3533215 1.6844726 1.9070303
## 84300903 1.296535 1.5784992 0.4557859 1.5651260 1.5575132
## 84348301 1.296535 -0.7682333 0.2535091 -0.5921661 -0.7637917
## 84358402 1.296535 1.7487579 -1.1508038 1.7750113 1.8246238
## 843786 1.296535 -0.4759559 -0.8346009 -0.3868077 -0.5052059
## smoothness_mean compactness_mean concavity_mean concave.points_mean
## 842302 1.5670875 3.2806281 2.65054179 2.5302489
## 842517 -0.8262354 -0.4866435 -0.02382489 0.5476623
## 84300903 0.9413821 1.0519999 1.36227979 2.0354398
## 84348301 3.2806668 3.3999174 1.91421287 1.4504311
## 84358402 0.2801253 0.5388663 1.36980615 1.4272370
## 843786 2.2354545 1.2432416 0.86554001 0.8239307
## symmetry_mean fractal_dimension_mean radius_se texture_se
## 842302 2.215565542 2.2537638 2.4875451 -0.5647681
## 842517 0.001391139 -0.8678888 0.4988157 -0.8754733
## 84300903 0.938858720 -0.3976580 1.2275958 -0.7793976
## 84348301 2.864862154 4.9066020 0.3260865 -0.1103120
## 84358402 -0.009552062 -0.5619555 1.2694258 -0.7895490
## 843786 1.004517928 1.8883435 -0.2548461 -0.5921406
## perimeter_se area_se smoothness_se compactness_se concavity_se
## 842302 2.8305403 2.4853907 -0.2138135 1.31570389 0.7233897
## 842517 0.2630955 0.7417493 -0.6048187 -0.69231710 -0.4403926
## 84300903 0.8501802 1.1802975 -0.2967439 0.81425704 0.2128891
## 84348301 0.2863415 -0.2881246 0.6890953 2.74186785 0.8187979
## 84358402 1.2720701 1.1893103 1.4817634 -0.04847723 0.8277425
## 843786 -0.3210217 -0.2890039 0.1562093 0.44515196 0.1598845
## concave.points_se symmetry_se fractal_dimension_se radius_worst
## 842302 0.66023900 1.1477468 0.90628565 1.8850310
## 842517 0.25993335 -0.8047423 -0.09935632 1.8043398
## 84300903 1.42357487 0.2368272 0.29330133 1.5105411
## 84348301 1.11402678 4.7285198 2.04571087 -0.2812170
## 84358402 1.14319885 -0.3607748 0.49888916 1.2974336
## 843786 -0.06906279 0.1340009 0.48641784 -0.1653528
## texture_worst perimeter_worst area_worst smoothness_worst
## 842302 -1.35809849 2.3015755 1.9994782 1.3065367
## 842517 -0.36887865 1.5337764 1.8888270 -0.3752817
## 84300903 -0.02395331 1.3462906 1.4550043 0.5269438
## 84348301 0.13386631 -0.2497196 -0.5495377 3.3912907
## 84358402 -1.46548091 1.3373627 1.2196511 0.2203623
## 843786 -0.31356043 -0.1149083 -0.2441054 2.0467119
## compactness_worst concavity_worst concave.points_worst symmetry_worst
## 842302 2.6143647 2.1076718 2.2940576 2.7482041
## 842517 -0.4300658 -0.1466200 1.0861286 -0.2436753
## 84300903 1.0819801 0.8542223 1.9532817 1.1512420
## 84348301 3.8899747 1.9878392 2.1738732 6.0407261
## 84358402 -0.3131190 0.6126397 0.7286181 -0.8675896
## 843786 1.7201029 1.2621327 0.9050914 1.7525273
## fractal_dimension_worst
## 842302 1.9353117
## 842517 0.2809428
## 84300903 0.2012142
## 84348301 4.9306719
## 84358402 -0.3967505
## 843786 2.2398308options(repr.plot.width = 15, repr.plot.height = 15) # set dimensions of plots
# Plot
corrplot(cor(ScaledMatrix),
#addCoef.col = 1, # color numeric labels
# = 0.8, # size numeric labels
order = "hclust",
tl.cex = 1, # size text label
addrect = 8) # draw rectangle
Exploratory Data Analysis
df%>%
group_by(diagnosis)%>%
summarize(
n = n())%>%
mutate(percent = paste0(round(100 * n/sum(n), 0), "%"))## # A tibble: 2 × 3
## diagnosis n percent
## <chr> <int> <chr>
## 1 B 357 63%
## 2 M 212 37%- insight Variabel hasil diagnosis memiliki kadar B untuk jinak dan M untuk ganas. Persentase kasus keganasan merupakan proporsi besar yang mengejutkan dari data dengan 37%.
Modelling
PCA
set.seed(1)
# Perform PCA on the scaled matrix and exclude "diagnosis"
pca <- prcomp(x= ScaledMatrix[,-c(1)], scale = TRUE, center = TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 3.6444 2.3857 1.67867 1.40735 1.28403 1.09880 0.82172
## Proportion of Variance 0.4427 0.1897 0.09393 0.06602 0.05496 0.04025 0.02251
## Cumulative Proportion 0.4427 0.6324 0.72636 0.79239 0.84734 0.88759 0.91010
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.69037 0.6457 0.59219 0.5421 0.51104 0.49128 0.39624
## Proportion of Variance 0.01589 0.0139 0.01169 0.0098 0.00871 0.00805 0.00523
## Cumulative Proportion 0.92598 0.9399 0.95157 0.9614 0.97007 0.97812 0.98335
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 0.30681 0.28260 0.24372 0.22939 0.22244 0.17652 0.1731
## Proportion of Variance 0.00314 0.00266 0.00198 0.00175 0.00165 0.00104 0.0010
## Cumulative Proportion 0.98649 0.98915 0.99113 0.99288 0.99453 0.99557 0.9966
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 0.16565 0.15602 0.1344 0.12442 0.09043 0.08307 0.03987
## Proportion of Variance 0.00091 0.00081 0.0006 0.00052 0.00027 0.00023 0.00005
## Cumulative Proportion 0.99749 0.99830 0.9989 0.99942 0.99969 0.99992 0.99997
## PC29 PC30
## Standard deviation 0.02736 0.01153
## Proportion of Variance 0.00002 0.00000
## Cumulative Proportion 1.00000 1.00000pca_res <- as.data.frame(pca$x[,1:7])# extract diagnosis column
diagnosis <- df_selected[c("diagnosis")]
row.names(diagnosis) <- df$id
# merge PCA results with diagnosis
PCA_data <- merge(diagnosis, pca_res,by=0, all=TRUE)%>%
select(-c("Row.names")) %>%
mutate(diagnosis = factor(diagnosis, levels = c("B", "M")))
# asign id as row names
row.names(PCA_data) <- df_selected$id
#head(PCA_data)
str(PCA_data)## 'data.frame': 569 obs. of 8 variables:
## $ diagnosis: Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ PC1 : num -9.18 -2.39 -5.73 -7.12 -3.93 ...
## $ PC2 : num -1.95 3.76 1.07 -10.27 1.95 ...
## $ PC3 : num -1.122 -0.529 -0.551 -3.23 1.389 ...
## $ PC4 : num 3.631 1.117 0.911 0.152 2.938 ...
## $ PC5 : num 1.194 -0.621 0.177 2.958 -0.546 ...
## $ PC6 : num 1.4102 0.0286 0.541 3.0507 -1.2254 ...
## $ PC7 : num 2.1575 0.0133 -0.6676 1.4287 -0.9354 ...Train Test Split
# scale data
sc_Matrix <- scale(Matrix)
# convert to df
Scaled_df <- as.data.frame(sc_Matrix )
# add diagnosis as categories
Scaled_df <- cbind(diagnosis = df[,2],Scaled_df[,-1])
# define diagnosis as factor
Scaled_df<- Scaled_df%>%
mutate(diagnosis = factor(diagnosis, levels = c("B","M")))
# split training and testing data
Scaled_df_index <- createDataPartition(Scaled_df$diagnosis, p=0.7, list = FALSE)
training_data <- Scaled_df[Scaled_df_index,]
testing_data <- Scaled_df[-Scaled_df_index,]
# split PCA data into training and testing data
PCA_data_index <- createDataPartition(PCA_data$diagnosis, p=0.7, list = FALSE)
training_data_PCA <- PCA_data[PCA_data_index,]
testing_data_PCA <- PCA_data[-PCA_data_index,]
#str(training_data_PCA)
#str(testing_data_PCA)Naive Bayes
set.seed(1)
model_NB <- naiveBayes(diagnosis~., data = training_data, laplace = 1)
summary(model_NB)## Length Class Mode
## apriori 2 table numeric
## tables 30 -none- list
## levels 2 -none- character
## isnumeric 30 -none- logical
## call 4 -none- calloptions(repr.plot.width = 5, repr.plot.height = 5) # set dimensions of plots
# model predictions
testing_data$predictions_NB <- predict(model_NB, testing_data)
predictions_NB <- predict(model_NB, testing_data)
#confusion matrix
CM_NB <- confusionMatrix(predictions_NB, testing_data$diagnosis, positive = "M")
CM_NB## Confusion Matrix and Statistics
##
## Reference
## Prediction B M
## B 103 7
## M 4 56
##
## Accuracy : 0.9353
## 95% CI : (0.8872, 0.9673)
## No Information Rate : 0.6294
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8599
##
## Mcnemar's Test P-Value : 0.5465
##
## Sensitivity : 0.8889
## Specificity : 0.9626
## Pos Pred Value : 0.9333
## Neg Pred Value : 0.9364
## Prevalence : 0.3706
## Detection Rate : 0.3294
## Detection Prevalence : 0.3529
## Balanced Accuracy : 0.9258
##
## 'Positive' Class : M
## # Plot confusion matrix
confusion_matrix <- data.frame(confusionMatrix(testing_data$predictions_NB, testing_data$diagnosis)$table)%>% rename(Observed = Reference)
ggplot(data = confusion_matrix, mapping = aes(x = Observed, y = Prediction)) +
labs(title = "Confusion matrix", subtitle = "") +
geom_tile(aes(fill = Freq), colour = "grey") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1, size = 7) +
scale_fill_gradient(low = '#009ADC', high = '#FF1F5B') +
theme_bw() + theme(legend.position = "none")
# Kesimpulan Tujuan dari analisis ini adalah untuk memprediksi diagnosis
sampel jaringan payudara dengan benar, berdasarkan 30 fitur jaringan.
Kami menemukan bahwa model Naive Bayes (92,94%). Secara keseluruhan, ini
semua adalah skor kinerja luar biasa yang menunjukkan bahwa jaringan
biopsi dapat menjadi sumber yang berharga untuk diagnosis kanker.